Application of the Monte Carlo method to estimate the uncertainty in the compressive strength test of high-strength concrete modelled with a multilayer perceptron
| Autor: | Pedro Celino Espinoza Haro, Luis Acuña Pinaud, Francisco García Fernández, Ana Victoria Torre Carrillo, Isabel Moromi Nakata |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Alternative methods
Artificial neural network Mathematical optimization Computer science Monte Carlo method 02 engineering and technology 021001 nanoscience & nanotechnology compressive strength high-strength concrete 020303 mechanical engineering & transports GUM 0203 mechanical engineering Stochastic simulation 0210 nano-technology Product standard uncertainty Monte Carlo High strength concrete |
| Zdroj: | Revista de la construcción v.17 n.2 2018 SciELO Chile CONICYT Chile instacron:CONICYT |
| Popis: | DOI: 10.7764/RDLC.17.2.319 The use of artificial neural networks as a modeling tool for the physic-mechanical properties of diverse materials has experienced great advances in the last ten years, mainly due to the increased in computing capacities of computers. This technique has been used in many different fields of science and its effectiveness is sufficiently proven. Its application in the particle board industry complies with the requirements of the test regulations for the use in production control, as an alternative method to normalized one. However, in spite of providing a result with a great approximation, they do not indicate anything about the uncertainty of the result. This last point is crucial when the results have to be compared with a product standard. There are internationally accepted deterministic techniques for obtaining the uncertainty of a test result, always starting from the knowledge of the function that relates the measure with the measurement parameters. However, these techniques are not entirely adequate for the case of excessively complex functions such as an artificial neural network. In these cases, the use of stochastic simulation methods such as the Monte Carlo method is more appropriate. In this article, an artificial neural network will be developed to obtain the compressive strength of high-strength concrete to later obtain the uncertainty by a Monte Carlo simulation. |
| Databáze: | OpenAIRE |
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